CYSMN DataSet1

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```r
install.packages(\data.table\)
library(data.table)

install.packages(\plotly\)
library(plotly)

install.packages(\stringr\)
library(stringr)

install.packages(\stringr\)
library(stringr)

install.packages(\plyr\)
library(plyr)

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<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Crawford 

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<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r
library(data.table)
library(plotly)
library(stringr)
library("moments")


#pre <- fread("CYSMN_Pre Survey_MODIFIED.csv")
post <- fread("CYSMN_Post Survey_MODIFIED.csv")

scentB <- post[ which(str_detect(post$Q19, "B")),]
scentA <- post[ which(str_detect(post$Q19, "A")),]


###******************************************************************###


############      Begin USER PERCEPTION Plots    ###############
# Begin Plots
UPQuestions <- c("Q1", "Q2", "Q3", "Q4", "Q13", "Q14")
UProsemary <- c(mean(scentA$Q1), 
              mean(scentA$Q2), 
              mean(scentA$Q3), 
              mean(scentA$Q4), 
              mean(scentA$Q13), 
              mean(scentA$Q14))

UPlavender <- c(mean(scentB$Q1), 
              mean(scentB$Q2), 
              mean(scentB$Q3),
              mean(scentB$Q4),
              mean(scentB$Q13),
              mean(scentB$Q14))


UPdata <- data.frame(UPQuestions, UProsemary, UPlavender)


# Calculate standard deviations
UPlavender_sd <- rbind(scentA$Q1, scentA$Q2, scentA$Q3, scentA$Q4, scentA$Q13, scentA$Q14)
UProsemary_sd <- rbind(scentB$Q1, scentB$Q2, scentB$Q3, scentB$Q4, scentB$Q13, scentB$Q14)

UPlavender_sd_final <- apply(UPlavender_sd, 1, function(x) sd(x))
UProsemary_sd_final <- apply(UProsemary_sd, 1, function(x) sd(x))

fig <- plot_ly(UPdata, x = ~UPQuestions, y = ~UProsemary, type = 'bar', name = 'Rosemary', error_y = ~list(array = UProsemary_sd_final, color = '#000000'))


fig <- fig %>% add_trace(y = ~UPlavender, name = 'Lavender', error_y = ~list(array = UPlavender_sd_final, color = '#000000'))


fig <- fig %>% layout(yaxis = list(title = 'Rating'), barmode = 'group')

fig

###******************************************************************###


############      Begin USER ACCEPTABILITY Plots    ###############

UAQuestions <- c("Q5", "Q6", "Q7", "Q8", "Q9", "Q10", "Q11", "Q12")
UArosemary <- c(mean(scentA$Q5), 
              mean(scentA$Q6), 
              mean(scentA$Q7), 
              mean(scentA$Q8), 
              mean(scentA$Q9), 
              mean(scentA$Q10),
              mean(scentA$Q11),
              mean(scentA$Q12))

UAlavender <- c(mean(scentB$Q5), 
              mean(scentB$Q6), 
              mean(scentB$Q7),
              mean(scentB$Q8),
              mean(scentB$Q9),
              mean(scentB$Q10),
              mean(scentB$Q11),
              mean(scentB$Q12))


UAdata <- data.frame(UAQuestions, UArosemary, UAlavender)


# Calculate standard deviations
UAlavender_sd <- rbind(scentA$Q5, 
                       scentA$Q6, 
                       scentA$Q7, 
                       scentA$Q8, 
                       scentA$Q9, 
                       scentA$Q10, 
                       scentA$Q11, 
                       scentA$Q12)

UArosemary_sd <- rbind(scentB$Q5, 
                       scentB$Q6, 
                       scentB$Q7, 
                       scentB$Q8, 
                       scentB$Q9, 
                       scentB$Q10, 
                       scentB$Q11, 
                       scentB$Q12)

UAlavender_sd_final <- apply(UAlavender_sd, 1, function(x) sd(x))
UArosemary_sd_final <- apply(UArosemary_sd, 1, function(x) sd(x))

fig <- plot_ly(UAdata, x = ~UAQuestions, y = ~UArosemary, type = 'bar', name = 'Rosemary', error_y = ~list(array = UArosemary_sd_final, color = '#000000'))


fig <- fig %>% add_trace(y = ~UAlavender, name = 'Lavender', error_y = ~list(array = UAlavender_sd_final, color = '#000000'))


fig <- fig %>% layout(yaxis = list(title = 'Rating'), barmode = 'group')

fig

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#Test Q1
wilcox.test(scentA$Q1, scentB$Q1, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q1 and scentB$Q1
W = 15.5, p-value = 0.6733
alternative hypothesis: true location shift is not equal to 0

#Test Q2
wilcox.test(scentA$Q2, scentB$Q2, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q2 and scentB$Q2
W = 15, p-value = 0.5948
alternative hypothesis: true location shift is not equal to 0

#Test Q3
wilcox.test(scentA$Q3, scentB$Q3, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q3 and scentB$Q3
W = 13, p-value = 0.4372
alternative hypothesis: true location shift is not equal to 0

#Test Q4
wilcox.test(scentA$Q4, scentB$Q4, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q4 and scentB$Q4
W = 9, p-value = 0.143
alternative hypothesis: true location shift is not equal to 0

#Test Q5
wilcox.test(scentA$Q5, scentB$Q5, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q5 and scentB$Q5
W = 10.5, p-value = 0.2259
alternative hypothesis: true location shift is not equal to 0

#Test Q6
wilcox.test(scentA$Q6, scentB$Q6, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q6 and scentB$Q6
W = 19.5, p-value = 0.8653
alternative hypothesis: true location shift is not equal to 0

#Test Q7
wilcox.test(scentA$Q7, scentB$Q7, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q7 and scentB$Q7
W = 19, p-value = 0.9313
alternative hypothesis: true location shift is not equal to 0

#Test Q8
wilcox.test(scentA$Q8, scentB$Q8, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q8 and scentB$Q8
W = 27, p-value = 0.1597
alternative hypothesis: true location shift is not equal to 0

#Test Q9
wilcox.test(scentA$Q9, scentB$Q9, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q9 and scentB$Q9
W = 12, p-value = 0.2824
alternative hypothesis: true location shift is not equal to 0

#Test Q10
wilcox.test(scentA$Q10, scentB$Q10, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q10 and scentB$Q10
W = 17, p-value = 0.9312
alternative hypothesis: true location shift is not equal to 0

#Test Q11
wilcox.test(scentA$Q11, scentB$Q11, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q11 and scentB$Q11
W = 20, p-value = 0.8055
alternative hypothesis: true location shift is not equal to 0

#Test Q12
wilcox.test(scentA$Q12, scentB$Q12, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q12 and scentB$Q12
W = 14.5, p-value = 0.6229
alternative hypothesis: true location shift is not equal to 0

#Test Q13
wilcox.test(scentA$Q13, scentB$Q13, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q13 and scentB$Q13
W = 21, p-value = 0.4047
alternative hypothesis: true location shift is not equal to 0

#Test Q14
wilcox.test(scentA$Q14, scentB$Q14, paired = FALSE)

cannot compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  scentA$Q14 and scentB$Q14
W = 23, p-value = 0.4533
alternative hypothesis: true location shift is not equal to 0

#Get Type A Questions

##############       COLUMN STATEMENTS          ###############
Q1A <- c(scentA$Q1)
Q13A <- c(scentA$Q13)
Q14A <- c(scentA$Q14)
Q2A <- c(scentA$Q2)
Q3A <- c(scentA$Q3)
Q4A <- c(scentA$Q4)
Q5A <- c(scentA$Q5)
Q6A <- c(scentA$Q6)
Q7A <- c(scentA$Q7)
Q8A <- c(scentA$Q8)
Q9A <- c(scentA$Q9)
Q10A <- c(scentA$Q10)
Q11A <- c(scentA$Q11)
Q12A <- c(scentA$Q12)


##############       AVERAGE STATEMENTS          ###############
avgQ1A <- mean(Q1A)
avgQ13A <- mean(Q13A)
avgQ14A <- mean(Q14A)
avgQ2A <- mean(Q2A)
avgQ3A <- mean(Q3A)
avgQ4A <- mean(Q4A)
avgQ5A <- mean(Q5A)
avgQ6A <- mean(Q6A)
avgQ7A <- mean(Q7A)
avgQ8A <- mean(Q8A)
avgQ9A <- mean(Q9A)
avgQ10A <- mean(Q10A)
avgQ11A <- mean(Q11A)
avgQ12A <- mean(Q12A)

##############       PRINT STATEMENTS          ###############
stringQ1A <- "Question 1 for Type A average: "
print(paste(stringQ1A, avgQ1A))

[1] "Question 1 for Type A average:  5.33333333333333"

print("")

[1] ""

stringQ2A <- "Question 2 for Type A average: "
print(paste(stringQ2A, avgQ2A))

[1] "Question 2 for Type A average:  5.16666666666667"

print("")

[1] ""

stringQ3A <- "Question 3 for Type A average: "
print(paste(stringQ3A, avgQ3A))

[1] "Question 3 for Type A average:  5.33333333333333"

print("")

[1] ""

stringQ4A <- "Question 4 for Type A average: "
print(paste(stringQ4A, avgQ4A))

[1] "Question 4 for Type A average:  4.66666666666667"

print("")

[1] ""

stringQ5A <- "Question 5 for Type A average: "
print(paste(stringQ5A, avgQ5A))

[1] "Question 5 for Type A average:  1.83333333333333"

print("")

[1] ""

stringQ6A <- "Question 6 for Type A average: "
print(paste(stringQ6A, avgQ6A))

[1] "Question 6 for Type A average:  6.16666666666667"

print("")

[1] ""

stringQ7A <- "Question 7 for Type A average: "
print(paste(stringQ7A, avgQ7A))

[1] "Question 7 for Type A average:  5.5"

print("")

[1] ""

stringQ8A <- "Question 8 for Type A average: "
print(paste(stringQ8A, avgQ8A))

[1] "Question 8 for Type A average:  3.83333333333333"

print("")

[1] ""

stringQ9A <- "Question 9 for Type A average: "
print(paste(stringQ9A, avgQ9A))

[1] "Question 9 for Type A average:  1.16666666666667"

print("")

[1] ""

stringQ10A <- "Question 10 for Type A average: "
print(paste(stringQ10A, avgQ10A))

[1] "Question 10 for Type A average:  5.83333333333333"

print("")

[1] ""

stringQ11A <- "Question 11 for Type A average: "
print(paste(stringQ11A, avgQ11A))

[1] "Question 11 for Type A average:  4"

print("")

[1] ""

stringQ12A <- "Question 12 for Type A average: "
print(paste(stringQ12A, avgQ12A))

[1] "Question 12 for Type A average:  4.16666666666667"

print("")

[1] ""

stringQ13A <- "Question 13 for Type A average: "
print(paste(stringQ13A, avgQ13A))

[1] "Question 13 for Type A average:  7"

print("")

[1] ""

stringQ14A <- "Question 14 for Type A average: "
print(paste(stringQ14A, avgQ14A))

[1] "Question 14 for Type A average:  3.5"

print("")

[1] ""

print("Type A Standard Deviation")

[1] "Type A Standard Deviation"

sd(scentA$Q1)

[1] 2.160247

sd(scentA$Q2)

[1] 0.4082483

sd(scentA$Q3)

[1] 1.75119

sd(scentA$Q4)

[1] 1.861899

sd(scentA$Q5)

[1] 1.602082

sd(scentA$Q6)

[1] 0.9831921

sd(scentA$Q7)

[1] 0.83666

sd(scentA$Q8)

[1] 1.47196

sd(scentA$Q9)

[1] 0.4082483

sd(scentA$Q10)

[1] 0.9831921

sd(scentA$Q11)

[1] 1.67332

sd(scentA$Q12)

[1] 1.47196

sd(scentA$Q13)

[1] 0

sd(scentA$Q14)

[1] 2.345208

#Get Type B Questions

##############       COLUMN STATEMENTS          ###############
Q1B <- c(scentB$Q1)
Q13B <- c(scentB$Q13)
Q14B <- c(scentB$Q14)
Q2B <- c(scentB$Q2)
Q3B <- c(scentB$Q3)
Q4B <- c(scentB$Q4)
Q5B <- c(scentB$Q5)
Q6B <- c(scentB$Q6)
Q7B <- c(scentB$Q7)
Q8B <- c(scentB$Q8)
Q9B <- c(scentB$Q9)
Q10B <- c(scentB$Q10)
Q11B <- c(scentB$Q11)
Q12B <- c(scentB$Q12)


##############       AVERAGE STATEMENTS          ###############
avgQ1B <- mean(Q1B)
avgQ13B <- mean(Q13B)
avgQ14B <- mean(Q14B)
avgQ2B <- mean(Q2B)
avgQ3B <- mean(Q3B)
avgQ4B <- mean(Q4B)
avgQ5B <- mean(Q5B)
avgQ6B <- mean(Q6B)
avgQ7B <- mean(Q7B)
avgQ8B <- mean(Q8B)
avgQ9B <- mean(Q9B)
avgQ10B <- mean(Q10B)
avgQ11B <- mean(Q11B)
avgQ12B <- mean(Q12B)

##############       PRINT STATEMENTS          ###############
stringQ1B <- "Question 1 for Type B average: "
print(paste(stringQ1B, avgQ1B))

[1] "Question 1 for Type B average:  6.16666666666667"

print("")

[1] ""

stringQ2B <- "Question 2 for Type B average: "
print(paste(stringQ2B, avgQ2B))

[1] "Question 2 for Type B average:  5.33333333333333"

print("")

[1] ""

stringQ3B <- "Question 3 for Type B average: "
print(paste(stringQ3B, avgQ3B))

[1] "Question 3 for Type B average:  6.16666666666667"

print("")

[1] ""

stringQ4B <- "Question 4 for Type B average: "
print(paste(stringQ4B, avgQ4B))

[1] "Question 4 for Type B average:  5.83333333333333"

print("")

[1] ""

stringQ5B <- "Question 5 for Type B average: "
print(paste(stringQ5B, avgQ5B))

[1] "Question 5 for Type B average:  2.33333333333333"

print("")

[1] ""

stringQ6B <- "Question 6 for Type B average: "
print(paste(stringQ6B, avgQ6B))

[1] "Question 6 for Type B average:  5.66666666666667"

print("")

[1] ""

stringQ7B <- "Question 7 for Type B average: "
print(paste(stringQ7B, avgQ7B))

[1] "Question 7 for Type B average:  5.16666666666667"

print("")

[1] ""

stringQ8B <- "Question 8 for Type B average: "
print(paste(stringQ8B, avgQ8B))

[1] "Question 8 for Type B average:  2.66666666666667"

print("")

[1] ""

stringQ9B <- "Question 9 for Type B average: "
print(paste(stringQ9B, avgQ9B))

[1] "Question 9 for Type B average:  1.5"

print("")

[1] ""

stringQ10B <- "Question 10 for Type B average: "
print(paste(stringQ10B, avgQ10B))

[1] "Question 10 for Type B average:  6"

print("")

[1] ""

stringQ11B <- "Question 11 for Type B average: "
print(paste(stringQ11B, avgQ11B))

[1] "Question 11 for Type B average:  3.66666666666667"

print("")

[1] ""

stringQ12B <- "Question 12 for Type B average: "
print(paste(stringQ12B, avgQ12B))

[1] "Question 12 for Type B average:  4.66666666666667"

print("")

[1] ""

stringQ13B <- "Question 13 for Type B average: "
print(paste(stringQ13B, avgQ13B))

[1] "Question 13 for Type B average:  6.83333333333333"

print("")

[1] ""

stringQ14B <- "Question 14 for Type B average: "
print(paste(stringQ14B, avgQ14B))

[1] "Question 14 for Type B average:  2.33333333333333"

print("")

[1] ""

print("Tye B Standard Deviation")

[1] "Tye B Standard Deviation"

sd(scentB$Q1)

[1] 0.4082483

sd(scentB$Q2)

[1] 0.5163978

sd(scentB$Q3)

[1] 0.7527727

sd(scentB$Q4)

[1] 0.9831921

sd(scentB$Q5)

[1] 1.36626

sd(scentB$Q6)

[1] 1.861899

sd(scentB$Q7)

[1] 1.722401

sd(scentB$Q8)

[1] 1.21106

sd(scentB$Q9)

[1] 0.5477226

sd(scentB$Q10)

[1] 0.8944272

sd(scentB$Q11)

[1] 2.160247

sd(scentB$Q12)

[1] 1.21106

sd(scentB$Q13)

[1] 0.4082483

sd(scentB$Q14)

[1] 1.75119

```r
```r

###############          Wilcox Testing for Type A         ####################
# testQ1 <- wilcox.test(Q1B, Q1A, mu=0, alt=\two.sided\, conf.int=T, conf.level=0.95, paired=T, exact=T, correct=T)

testQ1 <- t.test(Q1B, Q1A)
print(testQ1)

    Welch Two Sample t-test

data:  Q1B and Q1A
t = 0.92848, df = 5.3567, p-value = 0.3931
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.428383  3.095050
sample estimates:
mean of x mean of y 
 6.166667  5.333333 

```r
```r

#########           New data frame for Type A ###################
new_df_TA <- data.frame(Q1A, Q13A, Q14A, Q2A, Q3A, Q4A, Q5A, Q6A, Q7A, Q8A, Q9A, Q10A, Q11A, Q12A)

############     Skewed Values for Type A      #############
skew_Q1A <- skewness(new_df_TA$Q1A)
skew_Q13A <- skewness(new_df_TA$Q13A)
skew_Q14A <- skewness(new_df_TA$Q14A)
skew_Q2A <- skewness(new_df_TA$Q2A)
skew_Q3A <- skewness(new_df_TA$Q3A)
skew_Q4A <- skewness(new_df_TA$Q4A)
skew_Q5A <- skewness(new_df_TA$Q5A)
skew_Q6A <- skewness(new_df_TA$Q6A)
skew_Q7A <- skewness(new_df_TA$Q7A)
skew_Q8A <- skewness(new_df_TA$Q8A)
skew_Q9A <- skewness(new_df_TA$Q9A)
skew_Q10A <- skewness(new_df_TA$Q10A)
skew_Q11A <- skewness(new_df_TA$Q11A)
skew_Q12A <- skewness(new_df_TA$Q12A)

```r
```r

library(datasets)
data(iris)
library(plotly)
# https://plotly.com/r/line-and-scatter/